中圖分類號： TN919.2；TP181
文獻標識碼： A
DOI：10.16157/j.issn.0258-7998.211517
中文引用格式： 徐海兵,，郭久明. 基于雙向GRU模型的網絡流量預測的研究[J].電子技術應用,，2022，48(2)：19-22,，27.
英文引用格式： Xu Haibing,，Guo Jiuming. Research on network traffic prediction based on Bi-GRU model[J]. Application of Electronic Technique，2022,，48(2)：19-22,，27.

Research on network traffic prediction based on Bi-GRU model

Abstract： At present, there are some problems such as lag and low prediction accuracy when using gated recurrent units(GRU) neural network to predict traffic. This paper proposes an improved GRU model for traffic prediction. Firstly, based on GRU neural network, a network model integrating Bi-GRU neural network and artificial neural network is proposed, which satisfies the input of multi-dimensional vectors such as traffic features, time features and event features. At the same time, in order to improve the accuracy of some time periods, the training samples are classified into date classes, and a separate network model is generated for each type of date. It can greatly improve the accuracy of prediction and improve the lag of prediction. Finally, in order to improve the prediction accuracy of peak traffic, the experimental results show that the proposed goal can be achieved by the means of sample propensity balance and user-defined loss function.

Key words : traffic prediction,；neural network；gated recurrent unit,；loss function